{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding Multi-Agent Deep Deterministic Policy Gradient (MADDPG): A Complete Guide\n",
    "\n",
    "# Table of Contents\n",
    "\n",
    "- [Introduction: Multi-Agent Reinforcement Learning (MARL)](#introduction)\n",
    "- [The Challenge of Non-Stationarity in MARL](#challenge-non-stationarity)\n",
    "- [What is MADDPG?](#what-is-maddpg)\n",
    "  - [Core Idea: Centralized Training, Decentralized Execution](#core-idea)\n",
    "- [Why MADDPG? Advantages](#why-maddpg)\n",
    "- [Where and How MADDPG is Used](#where-and-how-maddpg-is-used)\n",
    "- [Mathematical Foundation of MADDPG](#mathematical-foundation-of-maddpg)\n",
    "  - [Centralized Action-Value Function (Critic)](#centralized-action-value-function-critic)\n",
    "  - [Critic Update](#critic-update)\n",
    "  - [Actor Update (Policy Gradient)](#actor-update-policy-gradient)\n",
    "  - [Target Networks and Soft Updates](#target-networks-and-soft-updates)\n",
    "  - [Exploration](#exploration)\n",
    "- [Step-by-Step Explanation of MADDPG](#step-by-step-explanation-of-maddpg)\n",
    "- [Key Components of MADDPG](#key-components-of-maddpg)\n",
    "  - [Per-Agent Actor Networks](#per-agent-actor-networks)\n",
    "  - [Per-Agent Centralized Critic Networks](#per-agent-centralized-critic-networks)\n",
    "  - [Target Networks (Actors & Critics)](#target-networks-actors--critics)\n",
    "  - [Replay Buffer (Shared)](#replay-buffer-shared)\n",
    "  - [Action Selection & Exploration](#action-selection--exploration)\n",
    "  - [Centralized Training Logic](#centralized-training-logic)\n",
    "  - [Hyperparameters](#hyperparameters)\n",
    "- [Practical Example: Custom Multi-Agent Grid World](#practical-example-custom-multi-agent-grid-world)\n",
    "  - [Environment Design Rationale](#environment-design-rationale)\n",
    "- [Setting up the Environment](#setting-up-the-environment)\n",
    "- [Creating the Custom Multi-Agent Environment](#creating-the-custom-multi-agent-environment)\n",
    "- [Implementing the MADDPG Algorithm](#implementing-the-maddpg-algorithm)\n",
    "  - [Defining the Actor Network (Discrete Actions)](#defining-the-actor-network-discrete-actions)\n",
    "  - [Defining the Centralized Critic Network](#defining-the-centralized-critic-network)\n",
    "  - [Defining the Replay Memory](#defining-the-replay-memory)\n",
    "  - [Soft Update Function](#soft-update-function)\n",
    "  - [MADDPG Agent Manager Class](#maddpg-agent-manager-class)\n",
    "  - [The MADDPG Update Step](#the-maddpg-update-step)\n",
    "- [Running the MADDPG Algorithm](#running-the-maddpg-algorithm)\n",
    "  - [Hyperparameter Setup](#hyperparameter-setup)\n",
    "  - [Initialization](#initialization)\n",
    "  - [Training Loop](#training-loop)\n",
    "- [Visualizing the Learning Process](#visualizing-the-learning-process)\n",
    "- [Analyzing the Learned Policies (Testing)](#analyzing-the-learned-policies-testing)\n",
    "- [Common Challenges and Extensions of MADDPG](#common-challenges-and-extensions-of-maddpg)\n",
    "- [Conclusion](#conclusion)\n",
    "\n",
    "## Introduction: Multi-Agent Reinforcement Learning (MARL)\n",
    "\n",
    "Multi-Agent Reinforcement Learning (MARL) extends RL to scenarios with multiple agents interacting within a shared environment. These agents might cooperate, compete, or have mixed objectives. MARL introduces unique challenges not present in single-agent settings.\n",
    "\n",
    "## The Challenge of Non-Stationarity in MARL\n",
    "\n",
    "A major difficulty arises when multiple agents learn simultaneously. From the perspective of any single agent, the environment appears *non-stationary* because the other agents' policies are constantly changing as they learn. An action that was good when other agents behaved one way might become bad when they adapt. This violates the stationarity assumption underlying many single-agent RL algorithms like standard Q-learning or DDPG, making learning unstable.\n",
    "\n",
    "## What is MADDPG?\n",
    "\n",
    "Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is an extension of the DDPG algorithm designed to address the non-stationarity problem in multi-agent settings. It utilizes the paradigm of **centralized training with decentralized execution**.\n",
    "\n",
    "### Core Idea: Centralized Training, Decentralized Execution\n",
    "\n",
    "1.  **Decentralized Execution:** During execution (acting in the environment), each agent $i$ selects its action $a_i$ based only on its *local* observation $o_i$ using its own actor network $\\mu_i(o_i; \\theta_i)$. This is crucial for practical deployment where agents might not have access to global information.\n",
    "2.  **Centralized Training:** During the training phase (updating the networks), MADDPG utilizes a *centralized critic* $Q_i$ for each agent $i$. This critic takes the *joint* observations (or states) of *all* agents ($x = (o_1, ..., o_N)$) and the *joint* actions of *all* agents ($a = (a_1, ..., a_N)$) as input to estimate the action-value $Q_i(x, a_1, ..., a_N; \\phi_i)$.\n",
    "\n",
    "By conditioning the critic on the joint state and actions, the learning target for each agent becomes stationary, even as other agents' policies change, because the critic knows what actions the other agents *actually took* (during training). This stabilizes the learning process.\n",
    "\n",
    "## Why MADDPG? Advantages\n",
    "\n",
    "- **Handles Non-Stationarity:** The centralized critic mitigates the primary challenge of multi-agent learning.\n",
    "- **Uses Only Local Information for Execution:** Policies can be deployed in scenarios where agents only have partial observability.\n",
    "- **No Assumptions on Environment Structure:** Unlike some MARL methods, it doesn't require specific communication protocols or knowledge of the reward structure (e.g., difference rewards).\n",
    "- **Handles Cooperative, Competitive, or Mixed Settings:** The framework is general.\n",
    "\n",
    "## Where and How MADDPG is Used\n",
    "\n",
    "MADDPG has been applied to various multi-agent domains:\n",
    "\n",
    "1.  **Cooperative Navigation:** Multiple agents coordinating to reach target locations while avoiding collisions.\n",
    "2.  **Predator-Prey Scenarios:** Agents learning to pursue or evade others.\n",
    "3.  **Traffic Signal Control:** Coordinating traffic lights.\n",
    "4.  **Multi-Robot Coordination:** Task allocation and path planning.\n",
    "\n",
    "It is particularly suitable when:\n",
    "- Agents have continuous or discrete action spaces (though originally presented for continuous).\n",
    "- Centralized training is feasible (access to joint information during learning phase).\n",
    "- Decentralized execution is required.\n",
    "- The environment dynamics are complex and potentially non-stationary from a single agent's view.\n",
    "\n",
    "## Mathematical Foundation of MADDPG\n",
    "\n",
    "MADDPG extends the DDPG framework to $N$ agents. Let the policies be $\\boldsymbol{\\mu} = \\{\\mu_1, ..., \\mu_N\\}$ parameterized by $\\boldsymbol{\\theta} = \\{\\theta_1, ..., \\theta_N\\}$, and the centralized action-value functions be $Q_i(x, a_1, ..., a_N; \\phi_i)$ parameterized by $\\phi_i$ for each agent $i$.\n",
    "\n",
    "### Centralized Action-Value Function (Critic)\n",
    "Each agent $i$ learns its own critic $Q_i(x, a_1, ..., a_N)$, where $x=(o_1, ..., o_N)$ represents the joint observations (or full state if available). This function estimates the expected return for *agent i*, given the joint context and actions.\n",
    "\n",
    "### Critic Update\n",
    "The critic $Q_i(x, a_1, ..., a_N; \\phi_i)$ for each agent $i$ is updated by minimizing the loss using samples from a shared replay buffer $\\mathcal{D}$. The loss is typically MSE:\n",
    "$$ L(\\phi_i) = \\mathbb{E}_{(x, a, r, x') \\sim \\mathcal{D}} [ (y_i - Q_i(x, a_1, ..., a_N; \\phi_i))^2 ] $$\n",
    "The target value $y_i$ is calculated using the *target* networks (denoted with a prime):\n",
    "$$ y_i = r_i + \\gamma Q'_i(x', a'_1, ..., a'_N; \\phi'_i)|_{a'_j = \\mu'_j(o'_j)} $$\n",
    "where $a'_j = \\mu'_j(o'_j; \\theta'_j)$ are the next actions predicted by each agent's *target* actor network based on their *next* local observations $o'_j$ from $x'$.\n",
    "\n",
    "### Actor Update (Policy Gradient)\n",
    "The actor $\\mu_i(o_i; \\theta_i)$ for each agent $i$ is updated using the deterministic policy gradient theorem, adapted for the centralized critic. The gradient ascent direction for actor $i$ is:\n",
    "$$ \\nabla_{\\theta_i} J(\\theta_i) \\approx \\mathbb{E}_{x, a \\sim \\mathcal{D}} [ \\nabla_{\\theta_i} \\mu_i(o_i; \\theta_i) \\nabla_{a_i} Q_i(x, a_1, ..., a_N; \\phi_i)|_{a_i=\\mu_i(o_i)} ] $$\n",
    "In practice, using PyTorch autograd, this is often achieved by minimizing the loss:\n",
    "$$ L(\\theta_i) = - \\mathbb{E}_{o_i \\sim \\mathcal{D}} [ Q_i(x, \\mu_1(o_1), ..., \\mu_N(o_N); \\phi_i) ] $$\n",
    "When calculating this loss and its gradient for actor $i$, the observations $o_j$ and actions $a_j = \\mu_j(o_j)$ for $j \\neq i$ are treated as fixed inputs obtained from the buffer or current policies.\n",
    "\n",
    "*Adaptation for Discrete Actions:* If using discrete actions, the actor typically outputs logits for a probability distribution (e.g., Categorical or Gumbel-Softmax). The update can then use a policy gradient approach where the advantage is derived from the centralized critic $Q_i$. For simplicity, one might use $Q_i(x, a_1, ..., a_N)$ directly as the signal (similar to how DDPG uses Q) to push up the probability of the action taken if the Q-value is high, although more sophisticated policy gradient estimates involving baselines are possible.\n",
    "\n",
    "### Target Networks and Soft Updates\n",
    "Similar to DDPG, MADDPG uses target networks for all actors ($\\mu'_i$) and critics ($Q'_i$), updated via soft updates:\n",
    "$$ \\theta'_i \\leftarrow \\tau \\theta_i + (1 - \\tau) \\theta'_i $$\n",
    "$$\\phi'_i \\leftarrow \\tau \\phi_i + (1 - \\tau) \\phi'_i $$\n",
    "\n",
    "### Exploration\n",
    "Exploration is typically handled by adding noise to the actions output by the deterministic actors during training rollouts. This is usually done independently for each agent (e.g., independent Ornstein-Uhlenbeck or Gaussian noise processes).\n",
    "For our discrete action adaptation, exploration can be achieved by sampling from the actor's output distribution or using $\\epsilon$-greedy based on the actor's preferred action.\n",
    "\n",
    "## Step-by-Step Explanation of MADDPG\n",
    "\n",
    "1.  **Initialize**: For each agent $i=1...N$:\n",
    "    - Actor network $\\mu_i(\\theta_i)$, Critic network $Q_i(\\phi_i)$.\n",
    "    - Target networks $\\mu'_i(\\theta'_i)$, $Q'_i(\\phi'_i)$ with $\\theta'_i \\leftarrow \\theta_i, \\phi'_i \\leftarrow \\phi_i$.\n",
    "    - Exploration noise process $\\mathcal{N}_i$.\n",
    "2.  **Initialize**: Shared Replay buffer $\\mathcal{D}$.\n",
    "3.  **For each training episode**:\n",
    "    a.  Get initial joint state/observations $x = (o_1, ..., o_N)$. Reset noise processes.\n",
    "    b.  **For each step $t$**:\n",
    "        i.   For each agent $i$, select action $a_i = \\mu_i(o_i; \\theta_i) + \\mathcal{N}_i$. Clip action if necessary.\n",
    "        ii.  Execute joint action $a = (a_1, ..., a_N)$, observe joint reward $r=(r_1, ..., r_N)$ and next joint state/observations $x'=(o'_1, ..., o'_N)$, and done flags $d=(d_1, ..., d_N)$.\n",
    "        iii. Store the *joint* transition $(x, a, r, x', d)$ in $\\mathcal{D}$.\n",
    "        iv.  $x \\leftarrow x'$.\n",
    "        v.   **Update Step (if buffer has enough samples)**:\n",
    "            1.  Sample a random mini-batch of $B$ joint transitions from $\\mathcal{D}$.\n",
    "            2.  For each agent $i=1...N$:\n",
    "                - **Update Critic $Q_i$**: Compute target $y_i$ using target networks $\\mu'_j, Q'_i$. Minimize $L(\\phi_i)$.\n",
    "                - **Update Actor $\\mu_i$**: Minimize actor loss $L(\\theta_i)$ using the main centralized critic $Q_i$. \n",
    "            3.  For each agent $i=1...N$:\n",
    "                - **Update Target Networks**: Perform soft updates on $\\theta'_i$ and $\\phi'_i$.\n",
    "        vi.  If any agent is done (or global termination), break step loop.\n",
    "4.  **Repeat**: Until convergence.\n",
    "\n",
    "## Key Components of MADDPG\n",
    "\n",
    "### Per-Agent Actor Networks\n",
    "- Each agent $i$ has an actor $\\mu_i$ mapping its local observation $o_i$ to its action $a_i$.\n",
    "- Trained using gradients from its associated centralized critic.\n",
    "\n",
    "### Per-Agent Centralized Critic Networks\n",
    "- Each agent $i$ has a critic $Q_i$ that estimates agent $i$'s action-value based on *joint* observations and *joint* actions $(x, a_1, ..., a_N)$.\n",
    "- Trained using target values derived from target networks.\n",
    "\n",
    "### Target Networks (Actors & Critics)\n",
    "- Slow-updating copies of all actor and critic networks used for stable target calculation.\n",
    "\n",
    "### Replay Buffer (Shared)\n",
    "- Stores tuples of joint experience: $(x, a, r, x', d)$.\n",
    "- Enables off-policy learning.\n",
    "\n",
    "### Action Selection & Exploration\n",
    "- Actors are used decentrally for execution, based only on local $o_i$.\n",
    "- Exploration noise (or sampling strategy) is added independently per agent during training.\n",
    "\n",
    "### Centralized Training Logic\n",
    "- The update step requires access to all agents' network parameters (for targets and critic inputs) and data from the shared buffer.\n",
    "\n",
    "### Hyperparameters\n",
    "- Similar to DDPG (buffer size, batch size, learning rates, $\\tau$, $\\gamma$, noise params), but potentially needed per agent or shared.\n",
    "\n",
    "## Practical Example: Custom Multi-Agent Grid World\n",
    "\n",
    "### Environment Design Rationale\n",
    "We'll create a simple 2-agent cooperative navigation task on a grid:\n",
    "- **State:** Positions of both agents and positions of their respective targets.\n",
    "- **Observation (Agent $i$):** Agent $i$'s position, Agent $i$'s target position, Other agent's position.\n",
    "- **Actions:** Discrete (Up, Down, Left, Right) for each agent.\n",
    "- **Reward:** Cooperative. Small negative reward per step. Large positive reward if *both* agents are simultaneously at their targets. Maybe a shaped reward based on distance reduction.\n",
    "- **Goal:** Both agents reach their targets concurrently.\n",
    "\n",
    "This allows us to demonstrate centralized Q-value estimation based on joint state (all positions) and joint actions, while actors act on local observations. We adapt MADDPG for discrete actions using policy gradients guided by the centralized Q-critic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up the Environment\n",
    "\n",
    "Import libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cpu\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import math\n",
    "from collections import namedtuple, deque\n",
    "from itertools import count\n",
    "from typing import List, Tuple, Dict, Optional, Callable, Any\n",
    "import copy\n",
    "\n",
    "# Import PyTorch\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.distributions import Categorical # For discrete actor\n",
    "\n",
    "# Set up device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# Set random seeds\n",
    "seed = 42\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the Custom Multi-Agent Environment\n",
    "\n",
    "A simple 2-agent cooperative grid navigation task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiAgentGridEnv:\n",
    "    \"\"\"\n",
    "    Simple 2-Agent Cooperative Grid World.\n",
    "    Agents must reach their respective goals simultaneously.\n",
    "    Observation includes agent positions and goal positions.\n",
    "    Rewards are shared.\n",
    "    \"\"\"\n",
    "    def __init__(self, size: int = 5, num_agents: int = 2):\n",
    "        self.size: int = size\n",
    "        self.num_agents: int = num_agents\n",
    "        self.action_dim: int = 4 # U, D, L, R\n",
    "        self.action_map: Dict[int, Tuple[int, int]] = {0: (-1, 0), 1: (1, 0), 2: (0, -1), 3: (0, 1)}\n",
    "        self.actions: List[int] = list(self.action_map.keys())\n",
    "\n",
    "        # Define fixed start and goal positions for simplicity\n",
    "        self.start_positions: List[Tuple[int, int]] = [(0, 0), (size - 1, size - 1)]\n",
    "        self.goal_positions: List[Tuple[int, int]] = [(size - 1, 0), (0, size - 1)]\n",
    "        if num_agents != 2:\n",
    "             raise NotImplementedError(\"Currently supports only 2 agents.\")\n",
    "\n",
    "        self.agent_positions: List[Tuple[int, int]] = list(self.start_positions)\n",
    "\n",
    "        # Observation space for agent i: (self_r, self_c, other_r, other_c, goal_i_r, goal_i_c)\n",
    "        # Normalized between 0 and 1\n",
    "        self.observation_dim_per_agent: int = 6 \n",
    "        self.max_coord = float(self.size - 1)\n",
    "\n",
    "    def _normalize_pos(self, pos: Tuple[int, int]) -> Tuple[float, float]:\n",
    "        \"\"\" Normalizes a grid position. \"\"\"\n",
    "        r, c = pos\n",
    "        return (r / self.max_coord, c / self.max_coord) if self.max_coord > 0 else (0.0, 0.0)\n",
    "        \n",
    "    def _get_observation(self, agent_id: int) -> np.ndarray:\n",
    "        \"\"\" Get normalized local observation for a specific agent. \"\"\"\n",
    "        obs = []\n",
    "        # Self position\n",
    "        self_pos_norm = self._normalize_pos(self.agent_positions[agent_id])\n",
    "        obs.extend(self_pos_norm)\n",
    "        # Other agent position\n",
    "        other_id = 1 - agent_id\n",
    "        other_pos_norm = self._normalize_pos(self.agent_positions[other_id])\n",
    "        obs.extend(other_pos_norm)\n",
    "        # Self goal position\n",
    "        goal_pos_norm = self._normalize_pos(self.goal_positions[agent_id])\n",
    "        obs.extend(goal_pos_norm)\n",
    "        return np.array(obs, dtype=np.float32)\n",
    "\n",
    "    def get_joint_observation(self) -> List[np.ndarray]:\n",
    "        \"\"\" Get list of observations for all agents. \"\"\"\n",
    "        return [self._get_observation(i) for i in range(self.num_agents)]\n",
    "        \n",
    "    def reset(self) -> List[np.ndarray]:\n",
    "        \"\"\" Resets environment, returns list of initial observations. \"\"\"\n",
    "        self.agent_positions = list(self.start_positions)\n",
    "        return self.get_joint_observation()\n",
    "\n",
    "    def step(self, actions: List[int]) -> Tuple[List[np.ndarray], List[float], bool]:\n",
    "        \"\"\"\n",
    "        Performs one step for all agents.\n",
    "        \n",
    "        Parameters:\n",
    "        - actions (List[int]): List of actions, one for each agent.\n",
    "        \n",
    "        Returns:\n",
    "        - Tuple[List[np.ndarray], List[float], bool]: \n",
    "            - List of next observations for each agent.\n",
    "            - List of rewards for each agent (shared reward).\n",
    "            - Global done flag.\n",
    "        \"\"\"\n",
    "        if len(actions) != self.num_agents:\n",
    "            raise ValueError(f\"Expected {self.num_agents} actions, got {len(actions)}\")\n",
    "            \n",
    "        next_positions: List[Tuple[int, int]] = list(self.agent_positions) # Start with current\n",
    "        total_dist_reduction = 0.0\n",
    "        hit_wall_penalty = 0.0\n",
    "\n",
    "        for i in range(self.num_agents):\n",
    "            current_pos = self.agent_positions[i]\n",
    "            if current_pos == self.goal_positions[i]: # Agent already at goal, doesn't move\n",
    "                continue \n",
    "                \n",
    "            action = actions[i]\n",
    "            dr, dc = self.action_map[action]\n",
    "            next_r, next_c = current_pos[0] + dr, current_pos[1] + dc\n",
    "            \n",
    "            # Check bounds and update position\n",
    "            if 0 <= next_r < self.size and 0 <= next_c < self.size:\n",
    "                next_positions[i] = (next_r, next_c)\n",
    "            else:\n",
    "                hit_wall_penalty -= 0.5 # Penalty for hitting wall\n",
    "                next_positions[i] = current_pos # Stay in place\n",
    "                \n",
    "        # Simple collision handling: if agents move to same spot, they bounce back\n",
    "        if self.num_agents == 2 and next_positions[0] == next_positions[1]:\n",
    "            next_positions = list(self.agent_positions) # Revert to previous positions\n",
    "            hit_wall_penalty -= 0.5 # Treat as a penalty (like hitting a wall)\n",
    "            \n",
    "        self.agent_positions = next_positions\n",
    "\n",
    "        # Calculate reward and done flag\n",
    "        done = all(self.agent_positions[i] == self.goal_positions[i] for i in range(self.num_agents))\n",
    "        \n",
    "        if done:\n",
    "            reward = 10.0 # Large reward for cooperative success\n",
    "        else:\n",
    "            # Distance-based reward (negative sum of distances to goals)\n",
    "            dist_reward = 0.0\n",
    "            for i in range(self.num_agents):\n",
    "                dist = abs(self.agent_positions[i][0] - self.goal_positions[i][0]) + \\\n",
    "                       abs(self.agent_positions[i][1] - self.goal_positions[i][1])\n",
    "                dist_reward -= dist * 0.1 # Scaled negative distance\n",
    "            \n",
    "            reward = -0.05 + dist_reward + hit_wall_penalty # Small step penalty + distance + wall penalty\n",
    "\n",
    "        next_observations = self.get_joint_observation()\n",
    "        # Shared reward\n",
    "        rewards = [reward] * self.num_agents \n",
    "        \n",
    "        return next_observations, rewards, done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate and test the multi-agent environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MADDPG Env Size: 5x5\n",
      "Num Agents: 2\n",
      "Action Dim per Agent: 4\n",
      "Obs Dim per Agent: 6\n",
      "Agent 0 Initial Obs: [0. 0. 1. 1. 1. 0.]\n",
      "Agent 1 Initial Obs: [1. 1. 0. 0. 0. 1.]\n",
      "\n",
      "After actions [1, 2]:\n",
      " Agent 0 Next Obs: [0.25 0.   1.   0.75 1.   0.  ]\n",
      " Agent 1 Next Obs: [1.   0.75 0.25 0.   0.   1.  ]\n",
      " Shared Reward: -0.8500000000000001\n",
      " Done: False\n"
     ]
    }
   ],
   "source": [
    "maddpg_env = MultiAgentGridEnv(size=5, num_agents=2)\n",
    "initial_obs_list = maddpg_env.reset()\n",
    "print(f\"MADDPG Env Size: {maddpg_env.size}x{maddpg_env.size}\")\n",
    "print(f\"Num Agents: {maddpg_env.num_agents}\")\n",
    "print(f\"Action Dim per Agent: {maddpg_env.action_dim}\")\n",
    "print(f\"Obs Dim per Agent: {maddpg_env.observation_dim_per_agent}\")\n",
    "print(f\"Agent 0 Initial Obs: {initial_obs_list[0]}\")\n",
    "print(f\"Agent 1 Initial Obs: {initial_obs_list[1]}\")\n",
    "\n",
    "# Example step\n",
    "actions_example = [1, 2] # Agent 0 Down, Agent 1 Left\n",
    "next_obs_list, rewards_list, done_flag = maddpg_env.step(actions_example)\n",
    "print(f\"\\nAfter actions {actions_example}:\")\n",
    "print(f\" Agent 0 Next Obs: {next_obs_list[0]}\")\n",
    "print(f\" Agent 1 Next Obs: {next_obs_list[1]}\")\n",
    "print(f\" Shared Reward: {rewards_list[0]}\")\n",
    "print(f\" Done: {done_flag}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing the MADDPG Algorithm\n",
    "\n",
    "### Defining the Actor Network (Discrete Actions)\n",
    "\n",
    "Outputs logits for a Categorical distribution over discrete actions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ActorDiscrete(nn.Module):\n",
    "    \"\"\" Actor Network for discrete actions in MADDPG. \"\"\"\n",
    "    def __init__(self, obs_dim: int, action_dim: int):\n",
    "        super(ActorDiscrete, self).__init__()\n",
    "        self.fc1 = nn.Linear(obs_dim, 128)\n",
    "        self.fc2 = nn.Linear(128, 128)\n",
    "        self.fc3 = nn.Linear(128, action_dim)\n",
    "\n",
    "    def forward(self, obs: torch.Tensor) -> Categorical:\n",
    "        \"\"\" Maps observation to action logits -> Categorical distribution. \"\"\"\n",
    "        x = F.relu(self.fc1(obs))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        action_logits = self.fc3(x)\n",
    "        return Categorical(logits=action_logits)\n",
    "        \n",
    "    def select_action(self, obs: torch.Tensor, use_exploration=True) -> Tuple[torch.Tensor, torch.Tensor]:\n",
    "        \"\"\" Selects action based on policy (sampling or argmax). \"\"\"\n",
    "        dist = self.forward(obs)\n",
    "        if use_exploration:\n",
    "            action = dist.sample()\n",
    "        else: # Deterministic for testing or target action prediction\n",
    "            action = torch.argmax(dist.probs, dim=-1)\n",
    "        log_prob = dist.log_prob(action)\n",
    "        return action, log_prob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Centralized Critic Network\n",
    "\n",
    "Takes joint observations and joint actions as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CentralizedCritic(nn.Module):\n",
    "    \"\"\" Centralized Critic Network for MADDPG. \"\"\"\n",
    "    def __init__(self, joint_obs_dim: int, joint_action_dim: int):\n",
    "        super(CentralizedCritic, self).__init__()\n",
    "        # Input size = size of all obs + size of all actions\n",
    "        self.fc1 = nn.Linear(joint_obs_dim + joint_action_dim, 256) \n",
    "        self.fc2 = nn.Linear(256, 256)\n",
    "        self.fc3 = nn.Linear(256, 1) # Output single Q-value for a specific agent\n",
    "\n",
    "    def forward(self, joint_obs: torch.Tensor, joint_actions: torch.Tensor) -> torch.Tensor:\n",
    "        \"\"\" Maps joint observations and actions to a Q-value. \"\"\"\n",
    "        # Ensure actions are in the correct format (e.g., one-hot for discrete)\n",
    "        # For simplicity here, we might pass action indices and handle inside or assume\n",
    "        # they are already processed (e.g., continuous or embedded).\n",
    "        # Let's assume actions are concatenated directly for simplicity here.\n",
    "        x = torch.cat([joint_obs, joint_actions], dim=1)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        q_value = self.fc3(x)\n",
    "        return q_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Replay Memory\n",
    "\n",
    "Stores joint transitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define transition structure for MADDPG buffer\n",
    "# Storing individual agent components separately is often easier for batching\n",
    "Experience = namedtuple('Experience', \n",
    "                        ('obs_list', 'action_list', 'reward_list', \n",
    "                         'next_obs_list', 'done_list'))\n",
    "\n",
    "# Replay Buffer (modified to handle lists of agent data)\n",
    "class MultiAgentReplayBuffer:\n",
    "    def __init__(self, capacity: int):\n",
    "        self.memory = deque([], maxlen=capacity)\n",
    "\n",
    "    def push(self, obs_list: List[np.ndarray], action_list: List[int], \n",
    "               reward_list: List[float], next_obs_list: List[np.ndarray], \n",
    "               done: bool) -> None:\n",
    "        \"\"\" Saves a joint transition. \"\"\"\n",
    "        # Convert numpy arrays to tensors (or store as numpy)\n",
    "        # Storing numpy might be slightly more memory efficient before batching\n",
    "        obs_t = [torch.from_numpy(o).float() for o in obs_list]\n",
    "        act_t = torch.tensor(action_list, dtype=torch.long) # Store action indices\n",
    "        rew_t = torch.tensor(reward_list, dtype=torch.float32)\n",
    "        next_obs_t = [torch.from_numpy(o).float() for o in next_obs_list]\n",
    "        done_t = torch.tensor([float(done)] * len(obs_list), dtype=torch.float32) # Repeat done flag for each agent\n",
    "        \n",
    "        self.memory.append(Experience(obs_t, act_t, rew_t, next_obs_t, done_t))\n",
    "\n",
    "    def sample(self, batch_size: int) -> Optional[Experience]:\n",
    "        \"\"\" Samples a batch of experiences. \"\"\"\n",
    "        if len(self.memory) < batch_size:\n",
    "            return None\n",
    "        \n",
    "        experiences = random.sample(self.memory, batch_size)\n",
    "        \n",
    "        # Batch processing: stack tensors for each component across the batch and agents\n",
    "        # Resulting shapes need careful handling in the update step\n",
    "        obs_batch, act_batch, rew_batch, next_obs_batch, dones_batch = zip(*experiences)\n",
    "        \n",
    "        # Example: Stack observations -> (batch_size, num_agents, obs_dim)\n",
    "        obs_batch_stacked = torch.stack([torch.stack(obs) for obs in obs_batch])\n",
    "        act_batch_stacked = torch.stack(act_batch) # (batch_size, num_agents)\n",
    "        rew_batch_stacked = torch.stack(rew_batch) # (batch_size, num_agents)\n",
    "        next_obs_batch_stacked = torch.stack([torch.stack(n_obs) for n_obs in next_obs_batch])\n",
    "        dones_batch_stacked = torch.stack(dones_batch) # (batch_size, num_agents)\n",
    "        \n",
    "        # Return a named tuple with batched tensors\n",
    "        return Experience(obs_batch_stacked, act_batch_stacked, rew_batch_stacked, \n",
    "                          next_obs_batch_stacked, dones_batch_stacked)\n",
    "\n",
    "    def __len__(self) -> int:\n",
    "        return len(self.memory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soft Update Function\n",
    "\n",
    "Standard soft update."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def soft_update(target_net: nn.Module, main_net: nn.Module, tau: float) -> None:\n",
    "    for target_param, main_param in zip(target_net.parameters(), main_net.parameters()):\n",
    "        target_param.data.copy_(tau * main_param.data + (1.0 - tau) * target_param.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MADDPG Agent Manager Class\n",
    "\n",
    "A class to manage the collection of agents, their networks, and optimizers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MADDPGAgentManager:\n",
    "    \"\"\" Manages multiple agents, their networks, and updates for MADDPG. \"\"\"\n",
    "    def __init__(self, num_agents: int, obs_dim_list: List[int], action_dim: int,\n",
    "                 actor_lr: float, critic_lr: float, gamma: float, tau: float,\n",
    "                 device: torch.device):\n",
    "        \n",
    "        self.num_agents = num_agents\n",
    "        self.obs_dim_list = obs_dim_list\n",
    "        self.action_dim = action_dim\n",
    "        self.gamma = gamma\n",
    "        self.tau = tau\n",
    "        self.device = device\n",
    "\n",
    "        # Calculate joint dimensions needed for critic\n",
    "        joint_obs_dim = sum(obs_dim_list)\n",
    "        # For discrete actions, critic needs appropriate representation (e.g., one-hot or indices)\n",
    "        # If passing indices, need embedding or careful handling. If one-hot, dim is num_agents * action_dim.\n",
    "        # Let's pass one-hot encoded actions to the critic.\n",
    "        joint_action_dim_one_hot = num_agents * action_dim\n",
    "\n",
    "        self.actors: List[ActorDiscrete] = []\n",
    "        self.critics: List[CentralizedCritic] = []\n",
    "        self.target_actors: List[ActorDiscrete] = []\n",
    "        self.target_critics: List[CentralizedCritic] = []\n",
    "        self.actor_optimizers: List[optim.Optimizer] = []\n",
    "        self.critic_optimizers: List[optim.Optimizer] = []\n",
    "\n",
    "        for i in range(num_agents):\n",
    "            # Create networks for agent i\n",
    "            actor = ActorDiscrete(obs_dim_list[i], action_dim).to(device)\n",
    "            critic = CentralizedCritic(joint_obs_dim, joint_action_dim_one_hot).to(device)\n",
    "            target_actor = ActorDiscrete(obs_dim_list[i], action_dim).to(device)\n",
    "            target_critic = CentralizedCritic(joint_obs_dim, joint_action_dim_one_hot).to(device)\n",
    "\n",
    "            # Initialize target networks\n",
    "            target_actor.load_state_dict(actor.state_dict())\n",
    "            target_critic.load_state_dict(critic.state_dict())\n",
    "            for p in target_actor.parameters(): p.requires_grad = False\n",
    "            for p in target_critic.parameters(): p.requires_grad = False\n",
    "\n",
    "            # Create optimizers\n",
    "            actor_optimizer = optim.Adam(actor.parameters(), lr=actor_lr)\n",
    "            critic_optimizer = optim.Adam(critic.parameters(), lr=critic_lr)\n",
    "            \n",
    "            # Store agent components\n",
    "            self.actors.append(actor)\n",
    "            self.critics.append(critic)\n",
    "            self.target_actors.append(target_actor)\n",
    "            self.target_critics.append(target_critic)\n",
    "            self.actor_optimizers.append(actor_optimizer)\n",
    "            self.critic_optimizers.append(critic_optimizer)\n",
    "\n",
    "    def select_actions(self, obs_list: List[torch.Tensor], use_exploration=True) -> Tuple[List[int], List[torch.Tensor]]:\n",
    "        \"\"\" Select actions for all agents based on their local observations. \"\"\"\n",
    "        actions = []\n",
    "        log_probs = [] \n",
    "        for i in range(self.num_agents):\n",
    "            self.actors[i].eval()\n",
    "            with torch.no_grad():\n",
    "                act, log_prob = self.actors[i].select_action(obs_list[i].to(self.device), use_exploration)\n",
    "                # Convert to scalar if it's a single observation\n",
    "                if act.dim() == 0:\n",
    "                    act = act.item()\n",
    "            self.actors[i].train()\n",
    "            actions.append(act)\n",
    "            log_probs.append(log_prob)\n",
    "        return actions, log_probs\n",
    "\n",
    "    def update(self, batch: Experience, agent_id: int) -> Tuple[float, float]:\n",
    "        \"\"\" Perform update for a single agent 'agent_id'. \"\"\"\n",
    "        # Unpack batch data\n",
    "        # Shapes: obs/next_obs(B, N, O_dim), acts(B, N),rews(B, N), dones(B, N)\n",
    "        obs_batch, act_batch, rew_batch, next_obs_batch, dones_batch = batch\n",
    "        batch_size = obs_batch.shape[0]\n",
    "\n",
    "        # Prepare inputs for the centralized critic\n",
    "        # Concatenate observations: (B, N, O) -> (B, N*O)\n",
    "        joint_obs = obs_batch.view(batch_size, -1).to(self.device)\n",
    "        joint_next_obs = next_obs_batch.view(batch_size, -1).to(self.device)\n",
    "\n",
    "        # Convert action indices to one-hot encoding for critic input\n",
    "        act_one_hot = F.one_hot(act_batch, num_classes=self.action_dim).float()\n",
    "        joint_actions_one_hot = act_one_hot.view(batch_size, -1).to(self.device)\n",
    "        \n",
    "        # Get rewards and dones for the current agent\n",
    "        rewards_i = rew_batch[:, agent_id].unsqueeze(-1).to(self.device)\n",
    "        dones_i = dones_batch[:, agent_id].unsqueeze(-1).to(self.device)\n",
    "        \n",
    "        # --- Critic Update --- \n",
    "        with torch.no_grad():\n",
    "            # Get next actions from ALL target actors\n",
    "            target_next_actions_list = []\n",
    "            for j in range(self.num_agents):\n",
    "                # Input: next observation for agent j across the batch (B, O_j)\n",
    "                obs_j_next = next_obs_batch[:, j, :].to(self.device)\n",
    "                # Get deterministic action from target actor j\n",
    "                action_j_next, _ = self.target_actors[j].select_action(obs_j_next, use_exploration=False)\n",
    "                # Convert action index to one-hot\n",
    "                action_j_next_one_hot = F.one_hot(action_j_next, num_classes=self.action_dim).float()\n",
    "                target_next_actions_list.append(action_j_next_one_hot)\n",
    "                \n",
    "            # Concatenate target next actions: (B, N*A_onehot)\n",
    "            joint_target_next_actions = torch.cat(target_next_actions_list, dim=1).to(self.device)\n",
    "\n",
    "            # Get target Q value from target critic i\n",
    "            q_target_next = self.target_critics[agent_id](joint_next_obs, joint_target_next_actions)\n",
    "            \n",
    "            # Calculate target y = r_i + gamma * Q'_i * (1 - d_i)\n",
    "            y = rewards_i + self.gamma * (1.0 - dones_i) * q_target_next\n",
    "\n",
    "        # Get current Q estimate from main critic i\n",
    "        q_current = self.critics[agent_id](joint_obs, joint_actions_one_hot)\n",
    "        \n",
    "        # Compute Critic loss\n",
    "        critic_loss = F.mse_loss(q_current, y)\n",
    "\n",
    "        # Optimize Critic i\n",
    "        self.critic_optimizers[agent_id].zero_grad()\n",
    "        critic_loss.backward()\n",
    "        torch.nn.utils.clip_grad_norm_(self.critics[agent_id].parameters(), 1.0) # Optional clipping\n",
    "        self.critic_optimizers[agent_id].step()\n",
    "\n",
    "        # --- Actor Update --- \n",
    "        # Freeze critic gradients for actor update\n",
    "        for p in self.critics[agent_id].parameters():\n",
    "            p.requires_grad = False\n",
    "\n",
    "        # Calculate actions for ALL agents using their CURRENT actors based on batch obs\n",
    "        current_actions_list = []\n",
    "        log_probs_i_list = [] # Store log_prob for agent i only\n",
    "        for j in range(self.num_agents):\n",
    "            obs_j = obs_batch[:, j, :].to(self.device)\n",
    "            # Need action probabilities/logits for the update - use Gumbel-Softmax or REINFORCE-like update\n",
    "            dist_j = self.actors[j](obs_j) # Get Categorical distribution\n",
    "            # If we use DDPG objective: Q(s, mu(s)), we need deterministic actions\n",
    "            # Let's adapt using policy gradient: Maximize E[log_pi_i * Q_i_detached]\n",
    "            action_j = dist_j.sample() # Sample action for input to Q\n",
    "            if j == agent_id:\n",
    "                 log_prob_i = dist_j.log_prob(action_j) # Only need log_prob for the agent being updated\n",
    "\n",
    "            action_j_one_hot = F.one_hot(action_j, num_classes=self.action_dim).float()\n",
    "            current_actions_list.append(action_j_one_hot)\n",
    "            \n",
    "        joint_current_actions = torch.cat(current_actions_list, dim=1).to(self.device)\n",
    "\n",
    "        # Calculate Actor loss: - E[log_pi_i * Q_i_detached]\n",
    "        # Q_i is evaluated with the *current* actions from all actors\n",
    "        q_for_actor_loss = self.critics[agent_id](joint_obs, joint_current_actions)\n",
    "        actor_loss = -(log_prob_i * q_for_actor_loss.detach()).mean() # Detach Q value \n",
    "        \n",
    "        # Alternative DDPG-style loss (if actors were deterministic): \n",
    "        # actor_loss = -self.critics[agent_id](joint_obs, joint_current_actions).mean()\n",
    "        \n",
    "        # Optimize Actor i\n",
    "        self.actor_optimizers[agent_id].zero_grad()\n",
    "        actor_loss.backward()\n",
    "        torch.nn.utils.clip_grad_norm_(self.actors[agent_id].parameters(), 1.0) # Optional clipping\n",
    "        self.actor_optimizers[agent_id].step()\n",
    "        \n",
    "        # Unfreeze critic gradients\n",
    "        for p in self.critics[agent_id].parameters():\n",
    "            p.requires_grad = True\n",
    "            \n",
    "        return critic_loss.item(), actor_loss.item()\n",
    "\n",
    "    def update_targets(self) -> None:\n",
    "        \"\"\" Perform soft updates for all target networks. \"\"\"\n",
    "        for i in range(self.num_agents):\n",
    "            soft_update(self.target_critics[i], self.critics[i], self.tau)\n",
    "            soft_update(self.target_actors[i], self.actors[i], self.tau)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the MADDPG Algorithm\n",
    "\n",
    "### Hyperparameter Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters for MADDPG on Custom Multi-Agent Grid World\n",
    "BUFFER_SIZE_MADDPG = int(1e5)\n",
    "BATCH_SIZE_MADDPG = 256\n",
    "GAMMA_MADDPG = 0.99\n",
    "TAU_MADDPG = 1e-3\n",
    "ACTOR_LR_MADDPG = 1e-4\n",
    "CRITIC_LR_MADDPG = 1e-3\n",
    "EPSILON_MADDPG_START = 1.0 # For epsilon-greedy exploration with discrete actions\n",
    "EPSILON_MADDPG_END = 0.05\n",
    "EPSILON_MADDPG_DECAY = 50000 # Decay over this many steps\n",
    "\n",
    "NUM_EPISODES_MADDPG = 200\n",
    "MAX_STEPS_PER_EPISODE_MADDPG = 100\n",
    "UPDATE_EVERY_MADDPG = 4      # Perform update every N environment steps\n",
    "NUM_UPDATES_MADDPG = 1       # Number of update passes per update interval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize Environment\n",
    "env_maddpg = MultiAgentGridEnv(size=5, num_agents=2)\n",
    "num_agents = env_maddpg.num_agents\n",
    "obs_dims = [env_maddpg.observation_dim_per_agent] * num_agents\n",
    "action_dim = env_maddpg.action_dim\n",
    "\n",
    "# Initialize Agent Manager\n",
    "maddpg_manager = MADDPGAgentManager(\n",
    "    num_agents=num_agents,\n",
    "    obs_dim_list=obs_dims,\n",
    "    action_dim=action_dim,\n",
    "    actor_lr=ACTOR_LR_MADDPG,\n",
    "    critic_lr=CRITIC_LR_MADDPG,\n",
    "    gamma=GAMMA_MADDPG,\n",
    "    tau=TAU_MADDPG,\n",
    "    device=device\n",
    ")\n",
    "\n",
    "# Initialize Replay Buffer\n",
    "memory_maddpg = MultiAgentReplayBuffer(BUFFER_SIZE_MADDPG)\n",
    "\n",
    "# Lists for plotting\n",
    "maddpg_episode_rewards = [] # Store total reward per episode (sum over agents)\n",
    "maddpg_critic_losses = [[] for _ in range(num_agents)] # List of lists for each agent's critic loss\n",
    "maddpg_actor_losses = [[] for _ in range(num_agents)] # List of lists for each agent's actor loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting MADDPG Training...\n",
      "Ep 50/200 | Steps: 4130 | Avg Reward: -65.08 | Avg C_Loss: 0.1754 | Avg A_Loss: -1.5070 | Eps: 0.922\n",
      "Ep 100/200 | Steps: 7739 | Avg Reward: -57.36 | Avg C_Loss: 0.0337 | Avg A_Loss: -2.1525 | Eps: 0.853\n",
      "Ep 150/200 | Steps: 11024 | Avg Reward: -47.13 | Avg C_Loss: 0.0143 | Avg A_Loss: -2.8037 | Eps: 0.791\n",
      "Ep 200/200 | Steps: 14686 | Avg Reward: -54.68 | Avg C_Loss: 0.0153 | Avg A_Loss: -3.7225 | Eps: 0.721\n",
      "Training Finished (MADDPG).\n"
     ]
    }
   ],
   "source": [
    "print(\"Starting MADDPG Training...\")\n",
    "\n",
    "total_steps_maddpg = 0\n",
    "epsilon = EPSILON_MADDPG_START\n",
    "\n",
    "for i_episode in range(1, NUM_EPISODES_MADDPG + 1):\n",
    "    obs_list_np = env_maddpg.reset()\n",
    "    obs_list = [torch.from_numpy(o).float().to(device) for o in obs_list_np]\n",
    "    episode_reward = 0\n",
    "    ep_critic_losses = [0.0] * num_agents\n",
    "    ep_actor_losses = [0.0] * num_agents\n",
    "    num_updates_ep = 0\n",
    "\n",
    "    for t in range(MAX_STEPS_PER_EPISODE_MADDPG):\n",
    "        # --- Action Selection --- \n",
    "        # Use epsilon-greedy for discrete actions adaptation\n",
    "        if random.random() < epsilon:\n",
    "            actions_list = [random.randrange(action_dim) for _ in range(num_agents)]\n",
    "        else:\n",
    "            # Get actions from actors (use_exploration=False gives argmax for testing)\n",
    "            actions_list, _ = maddpg_manager.select_actions(obs_list, use_exploration=False)\n",
    "        \n",
    "        # --- Environment Interaction --- \n",
    "        next_obs_list_np, rewards_list, done = env_maddpg.step(actions_list)\n",
    "        \n",
    "        # --- Store Experience (Joint) --- \n",
    "        memory_maddpg.push(obs_list_np, actions_list, rewards_list, next_obs_list_np, done)\n",
    "\n",
    "        # Update state representations\n",
    "        obs_list_np = next_obs_list_np\n",
    "        obs_list = [torch.from_numpy(o).float().to(device) for o in obs_list_np]\n",
    "        \n",
    "        # Accumulate shared reward for logging\n",
    "        episode_reward += rewards_list[0] # Using agent 0's reward as the shared reward\n",
    "        total_steps_maddpg += 1\n",
    "\n",
    "        # --- Update Networks --- \n",
    "        if len(memory_maddpg) > BATCH_SIZE_MADDPG and total_steps_maddpg % UPDATE_EVERY_MADDPG == 0:\n",
    "            for _ in range(NUM_UPDATES_MADDPG):\n",
    "                batch = memory_maddpg.sample(BATCH_SIZE_MADDPG)\n",
    "                if batch:\n",
    "                    for agent_id in range(num_agents):\n",
    "                        c_loss, a_loss = maddpg_manager.update(batch, agent_id)\n",
    "                        ep_critic_losses[agent_id] += c_loss\n",
    "                        ep_actor_losses[agent_id] += a_loss\n",
    "                    num_updates_ep += 1\n",
    "                    # Update target networks after updating all agents\n",
    "                    maddpg_manager.update_targets()\n",
    "\n",
    "        # Decay epsilon\n",
    "        epsilon = max(EPSILON_MADDPG_END, EPSILON_MADDPG_START - total_steps_maddpg / EPSILON_MADDPG_DECAY * (EPSILON_MADDPG_START - EPSILON_MADDPG_END))\n",
    "\n",
    "        if done:\n",
    "            break\n",
    "            \n",
    "    # --- End of Episode --- \n",
    "    maddpg_episode_rewards.append(episode_reward)\n",
    "    for i in range(num_agents):\n",
    "        maddpg_critic_losses[i].append(ep_critic_losses[i] / num_updates_ep if num_updates_ep > 0 else 0)\n",
    "        maddpg_actor_losses[i].append(ep_actor_losses[i] / num_updates_ep if num_updates_ep > 0 else 0)\n",
    "    \n",
    "    # Print progress\n",
    "    if i_episode % 50 == 0:\n",
    "        avg_reward = np.mean(maddpg_episode_rewards[-50:])\n",
    "        avg_closs = np.mean([np.mean(maddpg_critic_losses[i][-50:]) for i in range(num_agents)])\n",
    "        avg_aloss = np.mean([np.mean(maddpg_actor_losses[i][-50:]) for i in range(num_agents)])\n",
    "        print(f\"Ep {i_episode}/{NUM_EPISODES_MADDPG} | Steps: {total_steps_maddpg} | Avg Reward: {avg_reward:.2f} | Avg C_Loss: {avg_closs:.4f} | Avg A_Loss: {avg_aloss:.4f} | Eps: {epsilon:.3f}\")\n",
    "\n",
    "print(\"Training Finished (MADDPG).\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Learning Process\n",
    "\n",
    "Plot aggregate episode rewards and average losses across agents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting results for MADDPG\n",
    "plt.figure(figsize=(18, 4))\n",
    "\n",
    "# Episode Rewards (Aggregate)\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(maddpg_episode_rewards)\n",
    "plt.title('MADDPG GridWorld: Episode Rewards (Shared)')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Total Reward')\n",
    "plt.grid(True)\n",
    "if len(maddpg_episode_rewards) >= 50:\n",
    "    rewards_ma_maddpg = np.convolve(maddpg_episode_rewards, np.ones(50)/50, mode='valid')\n",
    "    plt.plot(np.arange(len(rewards_ma_maddpg)) + 49, rewards_ma_maddpg, label='50-episode MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Average Critic Loss (Across Agents)\n",
    "avg_critic_losses = np.mean(maddpg_critic_losses, axis=0)\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(avg_critic_losses)\n",
    "plt.title('MADDPG GridWorld: Avg Critic Loss / Episode')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Avg MSE Loss')\n",
    "plt.grid(True)\n",
    "if len(avg_critic_losses) >= 50:\n",
    "    closs_ma_maddpg = np.convolve(avg_critic_losses, np.ones(50)/50, mode='valid')\n",
    "    plt.plot(np.arange(len(closs_ma_maddpg)) + 49, closs_ma_maddpg, label='50-episode MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "# Average Actor Loss (Across Agents)\n",
    "avg_actor_losses = np.mean(maddpg_actor_losses, axis=0)\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(avg_actor_losses)\n",
    "plt.title('MADDPG GridWorld: Avg Actor Loss / Episode')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Avg Policy Gradient Loss')\n",
    "plt.grid(True)\n",
    "if len(avg_actor_losses) >= 50:\n",
    "    aloss_ma_maddpg = np.convolve(avg_actor_losses, np.ones(50)/50, mode='valid')\n",
    "    plt.plot(np.arange(len(aloss_ma_maddpg)) + 49, aloss_ma_maddpg, label='50-episode MA', color='orange')\n",
    "    plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis of MADDPG Learning Curves (GridWorld - Shared Reward):**\n",
    "\n",
    "1.  **Episode Rewards (Shared):**\n",
    "    *   **Observation:** The total shared reward per episode exhibits extreme volatility throughout the entire training period (200 episodes). While the 50-episode moving average (orange line) suggests a slight, gradual upward trend, indicating some learning is occurring, it remains significantly negative (hovering around -50 towards the end) and far from any potential optimal value (which would likely be positive in a goal-reaching task). There are large swings in performance from one episode to the next, without clear signs of stabilizing at a high reward level.\n",
    "    *   **Interpretation:** This plot highlights the primary challenge encountered: achieving stable and effective cooperative behavior. The high variance suggests that the decentralized actors are struggling to consistently coordinate their actions to achieve the shared goal. While the moving average shows *some* improvement over purely random behavior, the lack of convergence to high rewards indicates that finding a robust, cooperative joint policy is difficult under these conditions. The learning process is unstable, characteristic of the complexities introduced by multiple interacting agents.\n",
    "\n",
    "2.  **Avg Critic Loss / Episode:**\n",
    "    *   **Observation:** In stark contrast to the rewards, the average Mean Squared Error (MSE) loss for the centralized critic shows excellent convergence properties. After some initial high spikes (likely due to random initial policies and large prediction errors), the loss drops dramatically within the first ~50 episodes and converges steadily towards zero, remaining very low for the rest of the training.\n",
    "    *   **Interpretation:** This indicates that the centralized critic, which observes the actions and states of all agents, is effectively learning to predict the expected shared return (Q-value) for joint state-action pairs. The rapid convergence to low loss suggests the value estimation component of MADDPG is functioning well and is capable of accurately evaluating the consequences of the agents' collective actions based on the data it receives.\n",
    "\n",
    "3.  **Avg Actor Loss / Episode:**\n",
    "    *   **Observation:** The average actor loss, representing the policy gradient objective (often related to the Q-value estimated by the critic for the actor's chosen action), displays a clear and consistent downward trend (becoming more negative). This indicates that, on average, the actors are successfully adjusting their policies in directions that the critic suggests will lead to higher expected returns.\n",
    "    *   **Interpretation:** This plot confirms that the policy update mechanism is working as intended. Each decentralized actor is using the gradient information provided by the centralized critic to improve its individual policy. The steady decrease implies consistent policy improvement *according to the critic's current estimate*.\n",
    "\n",
    "**Overall Conclusion:**\n",
    "The MADDPG results present an interesting picture common in multi-agent learning. The underlying mechanisms – the centralized critic learning value functions and the decentralized actors updating policies based on critic guidance – appear to be functioning correctly, as shown by the converging loss plots. However, this internal learning progress does not translate into stable, high-reward team performance. The extreme volatility in shared rewards suggests significant challenges in coordination, credit assignment, or dealing with the non-stationary environment (where each agent's policy changes affect the optimal policy for others). While the critic learns *what* good joint actions look like, the decentralized actors struggle to consistently *discover and execute* those coordinated actions, leading to unstable overall results within the observed training duration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing the Learned Policies (Testing)\n",
    "\n",
    "Test the agents acting decentrally using their learned actors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Testing MADDPG Agents (5 episodes) ---\n",
      "Test Episode 1: Reward = -78.40, Length = 100\n",
      "Test Episode 2: Reward = -119.80, Length = 100\n",
      "Test Episode 3: Reward = -88.30, Length = 100\n",
      "Test Episode 4: Reward = -35.60, Length = 53\n",
      "Test Episode 5: Reward = -80.10, Length = 100\n",
      "--- Testing Complete. Average Reward: -80.44 ---\n"
     ]
    }
   ],
   "source": [
    "def test_maddpg_agents(manager: MADDPGAgentManager, \n",
    "                         env_instance: MultiAgentGridEnv, \n",
    "                         num_episodes: int = 5, \n",
    "                         seed_offset: int = 5000) -> None:\n",
    "    \"\"\" Tests the trained MADDPG agents acting decentrally. \"\"\"\n",
    "    print(f\"\\n--- Testing MADDPG Agents ({num_episodes} episodes) ---\")\n",
    "    all_episode_rewards = []\n",
    "\n",
    "    for i in range(num_episodes):\n",
    "        obs_list_np = env_instance.reset() # Use internal seeding if env supports it\n",
    "        obs_list = [torch.from_numpy(o).float().to(manager.device) for o in obs_list_np]\n",
    "        episode_reward = 0\n",
    "        done = False\n",
    "        t = 0\n",
    "\n",
    "        while not done and t < MAX_STEPS_PER_EPISODE_MADDPG: # Add step limit for testing\n",
    "            # Get actions decentrally, without exploration noise\n",
    "            actions_list, _ = maddpg_manager.select_actions(obs_list, use_exploration=True)\n",
    "            \n",
    "            # Step environment\n",
    "            next_obs_list_np, rewards_list, done = env_instance.step(actions_list)\n",
    "            \n",
    "            # Update observations\n",
    "            obs_list = [torch.from_numpy(o).float().to(manager.device) for o in next_obs_list_np]\n",
    "            \n",
    "            episode_reward += rewards_list[0] # Track shared reward\n",
    "            t += 1\n",
    "            \n",
    "        print(f\"Test Episode {i+1}: Reward = {episode_reward:.2f}, Length = {t}\")\n",
    "        all_episode_rewards.append(episode_reward)\n",
    "\n",
    "    print(f\"--- Testing Complete. Average Reward: {np.mean(all_episode_rewards):.2f} ---\")\n",
    "\n",
    "# Run test episodes\n",
    "test_maddpg_agents(maddpg_manager, env_maddpg, num_episodes=5)"
   ]
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    "## Common Challenges and Extensions of MADDPG\n",
    "\n",
    "**Challenge: Scalability of Centralized Critic**\n",
    "*   **Problem:** The input dimension of the centralized critic grows linearly with the number of agents (joint observations + joint actions). This can become computationally infeasible for a very large number of agents.\n",
    "*   **Solutions**:\n",
    "    *   **Parameter Sharing:** Use shared network parameters across critics (or actors) for similar agents.\n",
    "    *   **Attention Mechanisms:** Use attention to focus the critic on relevant information from other agents.\n",
    "    *   **Approximations:** Use methods that approximate the centralized critic without requiring full joint information (e.g., mean-field approaches).\n",
    "\n",
    "**Challenge: Credit Assignment in Cooperative Settings**\n",
    "*   **Problem:** When using a shared team reward, it's hard for an individual agent's critic to determine that agent's specific contribution to the team's success (the credit assignment problem).\n",
    "   **Solutions**:\n",
    "    *   **Counterfactual Baselines (COMA):** Compare the current joint Q-value with a baseline where only the current agent's action is varied.\n",
    "    *   **Value Decomposition Networks (VDN, QMIX):** Learn individual agent Q-functions that combine (e.g., sum) to form the joint Q-function, enforcing a relationship useful for credit assignment.\n",
    "\n",
    "**Challenge: Sensitivity to Hyperparameters**\n",
    "*   **Problem:** Like DDPG, MADDPG can be sensitive to learning rates, target updates, exploration noise, buffer size, etc.\n",
    "   **Solution:** Careful tuning is required, often specific to the MARL problem.\n",
    "\n",
    "**Challenge: Continuous Action Space Issues**\n",
    "*   **Problem:** The original MADDPG inherits potential instability issues from DDPG in continuous action spaces (e.g., Q-value overestimation).\n",
    "   **Solution:** Incorporate ideas from TD3 (e.g., twin centralized critics, delayed policy updates, target action smoothing) into the MADDPG framework (resulting in MATD3).\n",
    "\n",
    "**Extensions:**\n",
    "- **MATD3:** Incorporates TD3 improvements into MADDPG.\n",
    "- **MAPPO:** Adapts PPO for multi-agent settings, often using a centralized value function.\n",
    "- **Value Decomposition Methods (VDN, QMIX):** Focus on cooperative tasks with shared rewards.\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "MADDPG provides an effective framework for applying deep actor-critic methods to multi-agent environments. By leveraging centralized training (specifically through a centralized critic aware of joint information) and decentralized execution (actors using local observations), it successfully mitigates the non-stationarity challenge inherent in MARL.\n",
    "\n",
    "Its ability to handle various agent interactions (cooperative, competitive, mixed) and action spaces makes it a versatile algorithm. While challenges remain, particularly concerning scalability to very large numbers of agents and robust credit assignment, MADDPG represents a significant step in MARL and serves as a foundation for many subsequent multi-agent algorithms."
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